Survey of Image Object Recognition Techniques

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

www.irjet.net

e-ISSN: 2395 -0056 p-ISSN: 2395-0072

Survey of Image Object Recognition Techniques Gurjinder Singh1, Jaspreet Kaur2 12Guru

Gobind Singh College of Science and Engineering Guru Kashi University Talwandi Sabo (Bathinda), India

-------------------------------------------------------------------***----------------------------------------------------------------

Abstract- Object recognition is a process of detecting the

•

A robot could connect the objects it sees to the

object present in an image or a video sequence, with the help

information it knows about them, and also

of

connect new information about objects to what it

some

recognition

technique

or

methods.

Object

already knows about them

recognition is one of the techniques of digital image processing where we can process any image by applying

1.2 IMAGE MINING PROCESS

some of the operation. The modern world is enclosed with gigantic masses of digital visual information. Increase in the images has urged for the development of robust and efficient object recognition techniques. Most work reported in the literature focuses on competent techniques for object recognition and its applications.

Key words: Object recognition, Spatial, Temporal, Mining 1. INTRODUCTION Object recognition is the task of finding a given object in an image or video sequence. For any object in an image, there are many 'features' which are interesting points on the object that can be extracted to provide a "feature" description of the object. This description extracted from a

Fig-1: Data mining process interpreting mined results.

training image can then be used to identify the object when attempting to locate the object in a test image containing many other objects [1][2].

Data mining is the feature detection technique of image

Goals of Object Recognition

data and the use of software based techniques for finding image patterns and bitmap regularities in sets of image

•

•

Goal is to retrieve information that is not apparent

data. The computer program is responsible for observing

in the images we perceive. The name of things is

the image bit patterns by elucidating the pre-defined rules

one piece of information

and image features in the image data. Data collected from

Animals recognize without words. Important

different sources like databases or flat files or any other

information may be whether to ignore, eat, flee,

resources like web is made to be preprocessed. Various

etc.

standardization techniques of data cleaning or formatting

© 2017, IRJET

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 194

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

www.irjet.net

e-ISSN: 2395 -0056 p-ISSN: 2395-0072

are to be used by data warehouse or mapping schemes. [4]

one level of hierarchy to other. In [6] a set of two

Further preprocessed data is made to be reviewed by data

dimensional bits such as ‘00’ ‘11’ ‘01’ ‘10’ ‘111’ ‘100’ ‘101’

analyst who is done nowadays by computers software. In

‘001’ and ‘011’ is defined as primary relations for

earlier days it was manually done by data analysts. After

representing spatial region of image data coordinates in

reviewing preprocessed data analyst reports are to be

observed components of image. This method along with

made which is revised or refined if something is needed

2D stream of bits is suitable for sparse bit evaluation of

with hand on information to generate finally mined or

spatial relation in image patterns. The advantage of above

interpreted results.

method of representation of bits of image is that it left

2.

OBJECT

subtle denominations of spatial relations that are not

RECOGNITION TECHNIQUES ARE LISTED AS

required to be evaluated. However, the spatial relation is

SOME

OF

THE

IMPORTANT

not distance-dependent, non-interval-based bits in image

BELOW:

data are out of their scope of findings.

2.1 Spatial Relations One of the most mandatory features in many multimedia documents

is

management

of

spatial

coherence.

Management of spatial relation of components of image data is done by considering its rectangular Cartesian coordinates. The spatial axis position of a component in object in image data is represented by rectangular coordinates and the relations between components are calculated mathematically. [7, 3] A multimedia document Fig- 2: Check-board with one square colored incorrectly

which is composed of images, flowcharts, and other random graphics as well as image text is another example that proves the management of spatial coordinates for layout information in an image [5]. In other applications of image processing such as geographical information system (GIS), the image pattern representation and random indexing of abstract and finite spatial relations in some regions of image is studied. A 2D stream of bits [3], [7] is an image data indexing technique for representing a spatial pattern between different components of an image;

Fig-3: Blur image of check-board

2D stream of bits represents few patterns in position of components in neighboring regions, which is composed of horizontal and vertical ordering of image components.

Also, it gives several distinct levels of a sparse-strict spatial relation, where the stiffness of directional data differs from

© 2017, IRJET

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 195

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

www.irjet.net

e-ISSN: 2395 -0056 p-ISSN: 2395-0072

2.2 Temporal Relations

relations were discussed and extended by little and

A recent study depends upon bit representation and image

Ghafoor. Allen’s basic definition of temporal relations was

data management of temporal relation considers video

followed by Hopner.

based applications of image processing such as video

2.3 Data Retrieval In Conventional Databases

information databases (VID). There are two basic techniques for representing time based relationship

Earlier database systems were designed to manage and

between objects in multimedia data of a moving image:

retrieve the textual data and their related keyword based

One is a point-based image data representation containing

retrieval techniques which are not suitable for retrieving

time lag of various colors in a multimedia document, and

the data which comprises of text, video and audio data.

the other is an interval-based image data representation

Adding to this, in earlier times lot of human effort was

which contains intervals of data shifts from neighboring

required for the manual annotation because as the data

regions

based

which was stored in binary form was of no meaning to the

representation contains the Cartesian position of objects

human. Similarity comparison techniques are considered

by points in the image data on timeline, whereas the

to be the best techniques for the exact matching of the

interval-based representation contains the cohesion of

applications. To get the best solutions the mechanisms of

image objects by means of their intervals of the

filtering and ranking are used. The user can store and

occurrences of similar intensities in neighboring regions of

query various types of multimedia information with the

an image. Many studies shows

help of multimedia database system. The process of

of

data.

[12,

14,

18]

The

point

retrieving images which are exactly same to a given image

Interval based modeling technique of data representation.

can be done by the typical multimedia type. [8, 11, 9, 10]

is used to manage the time lag in between various

So that is why it is known as the content based retrieval

component videos objects that are used in multimedia

technique. This process can be carried out at three

document. Video elements of document (VID) are shown

different levels of abstraction. First one is raw data in

as if a stream of video bit patterns being defined by the

which the comparison is done by comparing each pixel

time lag intervals, a video object comprises of different

with the

streams of video frames which are stored in contiguous

other pixel. In this search is also done on the

different features of the objects like its texture etc. The

manner. [23, 24] Video objects can be changed using two

other is semantic in which the grouping of different objects

operations of merge and overlap. To be part of the

is done based on its meaning. The configurationally

definition of every video object the textual annotation of

similarity is based on the direction, topological address

the contents of the video are merged or overlapped. Both

and the relation of image object with respect to distance.

the frame based and interval based specification are used

The preprocessing is required by almost all the techniques

to query a video object in OVID. Gibbs et.al [11] used a

which are global or local.

timed based representation of component objects.

Sequential representation of component objects is used in this study. So this technique did not give a natural representation of a condition. Reverse and temporal

Š 2017, IRJET

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 196

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

e-ISSN: 2395 -0056

www.irjet.net

p-ISSN: 2395-0072

mining can only be obtained after the matching of the model description with the symbolic prescription.

2.5 Content Based Retrieval Technique In this technique a complete image is segmented into small parts which further comprise to form the group of small images based on the index and layout feature. For the large scale data management (AIR) Automated Image Retrieval System is used to develop the Image Science and machine Vision (ISMV) of ORNL. In the

Fig-4: Nine regions of interests in a binary encoding of relations

olden days textual description was handled manually. But there were different loopholes such as semantic structure can vary with respect to a retrieval task and it was also a

2.4 Image Extraction Through Mining

very time consuming process with regard to the size of

The technique of handling the association of image data

image databases. The solution to these problems was

and its patterns which are not stored in its images is

content descriptors. In these technique global dimensions

known as image mining. Different methods of image

of the image was used. [16, 18, 20] Due to this another

processing and retrieval are used by this technique. There

concept came into being that was named as Image

are two approaches to mining, firstly to mine from a large

segmentation in which the internal content of image was

database of images and the other one performed on

sidelined

different collections of images and the data. The co-

no

correct

technique

of

segmentation came into being i.e. it is a process with the

image mining is the production of different patterns with

help of which we can label each and every point of image

no knowledge of the image content. There can be different

including the class label. Another feature of segmentation

kinds of patterns like description, temporal, spatial etc.

is the capability to work in the unplanned sequence. These

[21, 23] All huge image databases are being handled but

techniques do not rely on continuous human efforts. This

the image mining. This system includes different image

problem is due to the restriction applied on the size. Due

retrieval as well as indexing technique for pattern

to everyday increase in the size of image which leads to

matching. This method is considered as if user friendly for

hindrance in the way of conversation. [12, 10, 13, 14] The

data patterns and can generate in depth knowledge of

gap between the low level and high level interpretation

image representation. The different patterns are grouped

can be decreased with the help of image segmentation. The

together .The sample of different images is taken to extract

low level image content should be user friendly so that

the content. But during this process different type of

changes can be performed at any stage in a image in CBIR

distortions and disturbances are being met. The results of

Impact Factor value: 5.181

is

we could not arrive at the best one. So a definition of

deteriorated by this technique. The actual meaning of

|

there

segmentation. Although there are different techniques but

ordination of structures and the human brain can be

Š 2017, IRJET

.But

|

ISO 9001:2008 Certified Journal

|

Page 197

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

www.irjet.net

e-ISSN: 2395 -0056 p-ISSN: 2395-0072

system. So different steps must be taken to reduce this gap.

The Semantic representation is based on color, spatial and texture information.

The deformities and ambiguities in different object recognition is due to spatial and spectral adjancies which are based on the type of shadow, object and their complex relations with the adjacent objects. [18] The object recognition can be improved with the use of a knowledge base. This process can be achieved by going through two stages, first one is to define the properties of each segmented region and the second one is to classify results based on the views of the classified objects. [15, 17, 19, 20] The scene contextual information is based on the height, Fig-6: Color Histogram image of water area

sensory information, the analysis of the visibility mapping etc. the methodology of object recognition is performed at two databases i.e. multi view digital angular camera and

2.6 Image Object Retrieval Using Context Aware

multi angular worldview

Technique The object recognition can be improved with the use of a knowledge base. This process can be achieved by going through two stages, first one is to define the properties of each segmented region and the second one is to classify results based on the views of the classified objects. The scene contextual information is based on the height, sensory information, the analysis of the visibility mapping etc. the methodology of object recognition is performed at two databases i.e. multi view digital angular camera and multi angular worldview. [22] The gap between the low level and high level interpretation can be decreased with

Fig-5: Oak Ridge image (2mĂ—2m)

the help of image segmentation. The low level image content should be user friendly so that changes can be performed at any stage in a image in CBIR system.

Š 2017, IRJET

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 198

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

e-ISSN: 2395 -0056

www.irjet.net

3. CONCLUSION

p-ISSN: 2395-0072

[7] Pettis and Jay, (2012), Getting Started with Image Tools, O'Reilly Media, New Delhi

In image pre-processing loaded input image undergoes segmentation, finding bright intensity values of pixels and

[8] Strack and Murtagh, (2008), Astronomical Image and

their belongingness to particular region is noted in the

Data Analysis, Springer

form of feature. Out of which extracted 2x1 matrix is

[9] Bottou, Cortes, Denker, Drucker, Guyon, Jackel, LeCun

maintained. This matrix shows presence of image object

and Muller,(1994) “Comparison of classifier methods: A

particles in an image. This algorithm is tested on light

case study in handwritten digit recognition,” in Proc. 12th

background images of courtesy standard benchmark

IAPR Int. Conf. Pattern Recognit., vol. 2, pp. 77–82.

images; this has found its moderate performance with these images as compared to other allied approaches like

[10] Phillips, Gleeson, (2001) "Volatile organic compounds

Convolution Based Detection,

in breath as markers of lung cancer:a cross-sectional study", Lancet , P.353:1930–33

REFERENCES

[11]

[1] V. Ferrari, T. Tuytelaars, and L.V. Gool(2004),

Yufeng

Zeng,

(2013),

Image Fusion and Its

Applications, Intech Press, Canada

“Simultaneous Object Recognition and Segmentation by Image Exploration,” Proc. Eighth European Conf. Computer

[12] Edwads, Staples, Thei zNose, (2000) "A New

Vision, 2004.

Electronic Nose Using Acoustic Technology", Acoustical Society of America, P.242-250

[2] B. Leibe, A. Leonardis, and B. Schiele(2004), “Combined Object Categorization and Segmentation with an Implicit

[13] Berg, Olsson, Lindblad, and Chilo, (2008) “Automatic

Shape Model,” Proc. ECCV Workshop Statistical Learning in

design of pulse coupled neurons for image segmentation,”

Computer Vision, 2004

Neurocomputing, vol. 71, nos. 10–12, pp. 1980–1993.

[3] Burger and Lenddins, (2010), Digital Image processing

[14] Cao, Luo, Kautz, and Huang, (2009) “Image annotation

using Matlab, Mcgraw Hill's New Delhi

within the context of personal photo collections using hierarchical event and scene models,” IEEE Trans.

[4] Dwayne and Philipps, (2000), Analyzing and Enhancing

Multimedia, vol. 11, no. 2, pp. 208–219

Digital Images, R & D Books, US

[15] Wang, Hua, Tang, and Hong,(2009) “Beyond distance

[5] Hanry and Ford, (2010), Fundamentals of image

measurement: Constructing neighborhood similarity for

Processing, Dartmouth Inst Press, US

video annotation,” IEEE Trans. Multimedia, vol. 11, no. 3, pp. 465–476

[6] Koprowsky, Wrobel and Ketty, (2011), Image Processing in Optical Coherence Tomography, University of

[16] Afraz and Cavanagh, (2009)."The gender specific face

Silesia, US

© 2017, IRJET

after effect is based in retinotopic not spatiotopic

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 199

International Research Journal of Engineering and Technology (IRJET) Volume: 04 Issue: 01 | Jan -2017

www.irjet.net

e-ISSN: 2395 -0056 p-ISSN: 2395-0072

BIOGRAPHIES

coordinates across several natural image transformations."

J.Vis. 9,10.1–10.17.doi:10.1167/9.10.10

Gurjinder Singh received BCA in 2012 and Msc (IT) in 2014 Under GNDU(Guru Nanak Dev University, Amritsar).Now His M.Tech(IT) perusing From Guru Kashi University ,Talwandi Sabo (Bathinda),India.

[17] Everingham, Gool, Williams, Winn, and Zisserman, (2010) “The Pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis., vol. 88, no. 2, pp. 303–338.

[18]

Laeng,

Okubo,

Saneyoshi,

Michimata,

(2011),

"Processing spatial relations with different apertures of attention", Cogn Sci35:297–329 [19] Arbelaez, Maire, Fowlkes, and Malik, (2011) “Contour Detection and Hierarchical Image Segmentation,” PAMI, vol. 33, no. 5.

[20] Chen, Park, Ma, and Ala, (2011) “A new automatic parameter setting method of a simplified PCNN for image segmentation,” IEEE Trans. Neural Netw., vol. 22, no. 6, pp.

880–892.

[21] Wei,

Wang, and Lai, (2012) “Compact image

representation model based on both nCRF and reverse control mechanisms,” IEEE Trans. Neural Netw. Learn.

Syst., vol. 23, no. 1, pp. 150–162.

[22] L.Tadic, (2012) “Color Space Basics”, AMIA Tech Review. Issue 4. pno 123 [23] Westland S. (2012) “Computational Colour Science using MATLAB 2e".By WILEY ISBN: 978-0-470-66569-5 p:27-104.

[24] Wu, Chensheng, Christopher, Davis.(2014) "Modified plenoptic camera for phase and amplitude wavefront sensing." International Society for Optics and Photonics, pp. 88740I-88740I.

© 2017, IRJET

|

Impact Factor value: 5.181

|

ISO 9001:2008 Certified Journal

|

Page 200